Abstract:

The type of ground faults in distribution networks is complex, and the amounts of faults is often intermittent and weak. Based on traditional machine learning methods, it is difficult to mine the effective information of the amounts of faults, which limits the improvement of fault identification performance. Aiming at this problem, a method to identify ground faults based on convolutional neural network (CNN) to extract fault features automatically is proposed. PSCAD software builds a simulation model of the grounding fault of the distribution network, and the massive sample set required by CNN is generated by the script program. Build a CNN framework, use convolution and pooling operations to extract the characteristics of the fault amount to accurately characterize the identified fault type. The ability of CNN to extract fault features is demonstrated through t-SNE method. The adjustment of CNN key parameters and the selection of optimization algorithms are done to improve the model convergence speed and identification performance. The results show that, compared with traditional machine learning methods, the CNN model has the highest identification accuracy and is not disturbed by frequency, fault location, voltage fluctuations, etc. Its fault tolerance and adaptability are verified in the case of the distribution network information loss and harmonic injection.

Key words: Distribution network, fault identification, convolutional neural network, PSCAD, data visualization